@inproceedings{ruder-sil-2021-multi,
    title = "Multi-Domain Multilingual Question Answering",
    author = "Ruder, Sebastian  and
      Sil, Avi",
    editor = "Jiang, Jing  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic {\&} Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-tutorials.4/",
    doi = "10.18653/v1/2021.emnlp-tutorials.4",
    pages = "17--21",
    abstract = "Question answering (QA) is one of the most challenging and impactful tasks in natural language processing. Most research in QA, however, has focused on the open-domain or monolingual setting while most real-world applications deal with specific domains or languages. In this tutorial, we attempt to bridge this gap. Firstly, we introduce standard benchmarks in multi-domain and multilingual QA. In both scenarios, we discuss state-of-the-art approaches that achieve impressive performance, ranging from zero-shot transfer learning to out-of-the-box training with open-domain QA systems. Finally, we will present open research problems that this new research agenda poses such as multi-task learning, cross-lingual transfer learning, domain adaptation and training large scale pre-trained multilingual language models."
}